Post-Doctoral Research Fellow - Systems Biology

Post-Doctoral Research Fellow - Systems Biology Job ID 31048 Type Regular Full-Time Location US-WA-Seattle Category Post-Doctoral Research Fellows and Associates Overview Fred Hutchinson Cancer Center is an independent, nonprofit organization providing adult cancer treatment and groundbreaking research focused on cancer and infectious diseases. Based in Seattle, Fred Hutch is the only National Cancer Institute-designated cancer center in Washington. With a track record of global leadership in bone marrow transplantation, HIV/AIDS prevention, immunotherapy and COVID-19 vaccines, Fred Hutch has earned a reputation as one of the world's leading cancer, infectious disease and biomedical research centers. Fred Hutch operates eight clinical care sites that provide medical oncology, infusion, radiation, proton therapy and related services, and network affiliations with hospitals in five states. Together, our fully integrated research and clinical care teams seek to discover new cures to the world's deadliest diseases and make life beyond cancer a reality. At Fred Hutch we value collaboration, compassion, determination, excellence, innovation, integrity and respect. Our mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us stronger. We seek employees who bring different and innovative ways of seeing the world and solving problems. The Gujral Lab ( works at the intersection of systems biology, functional genomics, and precision oncology. The lab focuses on understanding how complex signaling networks and cellular states drive cancer progression and therapeutic response (Nat Biotech, 2026), with a particular emphasis on rare and refractory cancers. By integrating high-throughput drug screening (Cell Rep Medicine, 2025, Gut, 2025), machine learning (PNAS, 2021, MSB, 2021), and multiomic data (PNAS, 2025), the Gujral Lab aims to uncover actionable vulnerabilities and develop more effective, personalized treatment strategies. The overarching goal is to translate systems-level insights into clinically relevant advances in cancer care. The research environment is multidisciplinary, spanning clinical, translational, and basic science. The lab leverages resources from the TRACER program, including access to patient-derived models, fresh tumor specimens through clinical collaborations, and engineered murine models. Together, these resources enable innovative, data-driven approaches to studying and treating rare cancers. The Gujral Lab is seeking a highly motivated and innovative Post-Doctoral Research Fellow to advance precision oncology through systems biology approaches that integrate functional screening, genomics, and computational modeling. Responsibilities The successful candidate will study the molecular and systems-level mechanisms driving cancer progression and therapeutic response, with an emphasis on identifying actionable vulnerabilities through data-driven approaches. This role sits at the interface of experimental and computational biology, leveraging technologies such as high-throughput drug screening, functional genomics, and multi-omic data integration. This position offers a highly collaborative environment, with opportunities to work alongside multidisciplinary teams in functional genomics, cancer biology, and in vivo modeling. The fellow will contribute to translating complex biological data into clinically meaningful insights. Qualifications MINIMUM QUALIFICATIONS: Recent or soon to be PhD, MD, or MD/PhD (or equivalent) in systems biology, computational biology, cancer biology, bioinformatics, or a related field Strong foundation in cell and molecular biology, with interest in systems-level analysis Demonstrated ability to analyze complex datasets and integrate multi-modal data Excellent written and oral communication skills Track record of rigorous and productive research, including at least one first-author publication in a peer-reviewed journal Ability to independently design, execute, and interpret research studies PREFERRED QUALIFICATIONS: Experience with functional genomics and/or high-throughput drug screening Background in computational biology, bioinformatics, or machine learning Experience working with in vivo models or patient-derived systems The annual base salary range for this position is from $80,172 to $95,014, and pay offered will be based on experience and qualifications. This position may be eligible for relocation assistance. Although Fred Hutch is not sponsoring most H-1B visas at this time, candidates who already hold an H-1B sponsored by another organization and are currently in the U.S. may be eligible for this position. Fred Hutchinson Cancer Center offers employees a comprehensive benefits package designed to enhance health, well-being, and financial security. Benefits include medical/vision, dental, flexible spending accounts, life, disability, retirement, family life support, employee assistance program, onsite health clinic, income-based child care subsidy, tuition reimbursement, paid vacation (22 days per year), paid sick leave (up to 30 calendar days per occurrence of a qualifying reason), paid holidays (13 days per year), and paid parental leave (up to 4 weeks). Additional Information We are proud to be an Equal Employment Opportunity (EEO) and Vietnam Era Veterans Readjustment Assistance Act (VEVRAA) Employer. We do not discriminate on the basis of race, color, religion, creed, ancestry, national origin, sex, age, disability (physical or mental), marital or veteran status, genetic information, sexual orientation, gender identity, political ideology, or membership in any other legally protected class. We desire priority referrals of protected veterans. If due to a disability you need assistance/and or a reasonable accommodation during the application or recruiting process, please send a request to Human Resources at View email address on click.appcast.io or by calling View phone number on click.appcast.io.